Syntekabio’s Repurposed COVID-19 Combination Therapy Shows Notable Animal Efficacy

 

  • Filed a patent application on the new use of the drugs after in vitro comparative study against Remdesivir in collaboration with a national research institute at ’20 2Q
  • The two drugs, derived from in silico simulation, were administered together to laboratory animals. Results showed the combination therapy demonstrated lung pathogenicity recovery rate of 94.3%, while the Remdesivir showed 44.3%
  • Ongoing negotiation with clinical development and regulatory submission partners for several countries   

Syntekabio, an AI and NGS-based drug development company, announced that their COVID-19 combination therapy candidates yielded lung pathogenicity recovery rate of 94.3%, while the Remdesivir showed recovery rate of 44.3%. This animal efficacy study was a collaborative effort with one of the South Korea’s national research institutes.

Early February, the structure of 3CL hydrolase (Mpro) was first published in the Protein Data Bank (PDB)[1]. 3CL hydrolase is an enzyme that plays a key role in gene expression and proliferation of the COVID-19 virus, thereby being an attractive target for development of COVID-19 therapeutics. Based on the structural information of 3CL hydrolase, Syntekabio selected 30 drug candidates by screening more than 3,000 FDA approved drugs, utilizing supercomputer and proprietary AI-based synthetic drug discovery platform. Subsequent in-vitro study narrowed down the initial candidate pool to 3 drugs that exhibited in vitro efficacy to a level comparable to that of the remedesivir. Combination of 2 drugs showed remarkable lung pathogenicity recovery rate compared to those of Remdesivir, on animal models.

These substances have been used for more than 20 years, thus with long-term safety data compared to newer drugs. No serious adverse events were observed on this animal efficacy study.

Regarding this drug re-purposing and research, Sunil Youn, MD, Director of Business Development, said, “The animal efficacy result seems promising, but we need more evidence to be administered to the COVID-19 patients. We are on discussion with prospective partners regarding to the clinical trials, regulatory submission. Sooner or later, we hope to present future development plan and patient access schedule.”

Reference

[1]      Z. Jin et al., “Structure of Mpro from COVID-19 virus and discovery of its inhibitors,” bioRxiv, 2020, doi: 10.1101/2020.02.26.964882.

About Syntekabio, Inc.

Syntekabio is an AI and NGS based drug development company, utilizing genomic database and artificial intelligence to predict and identify new molecular entities to be a relevant new drug product. It is the global first AI drug development company listed on the public market (KOSDAQ: 226330) last December. The Company’s lead product candidate, STB-C017, an IDO/TDO dual inhibitor for the treatment of advanced solid tumor, is under nonclinical development. The company’s subsequent pipelines include personalized neoantigen cancer vaccines, small molecules targeting established oncology targets, and biomarkers to stratify relevant patients to maximize treatment efficacy. Syntekabio’s business model is to collaborate with various academic institutions and biopharma companies to optimize development process, utilizing proprietary AI and NGS data. The Company is headquartered in Daejeon, Rep. of Korea, with offices in Seoul, Rep. of Korea and Rockville, MD, USA. For additional information, please visit webpage: http://www.syntekabio.com/

About DeepMatcherTM

Based on Syntekabio’s proprietary artificial intelligence technology focused on 3D protein and chemical structure prediction, utilizing genomic big data, DeepMatcherTM offers several solutions for biopharma industry and academic institutions.

Small molecule lead discovery: AI based chemical lead discovery, secondary and/or off-target effect analysis, resistant biomarker identification. Could be utilized on drug repurposing and de novo molecule design.

Neoantigen prediction: Personalized or common neoantigen-based cancer immunotherapy powered by deep learning driven MHC-peptide binding prediction (proven accuracy of 94% Positive Predictive Value). Prospective co-development may derive de novo personalized neoantigen therapeutics.

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